Some recent works propose generalizable approaches to adapting impedance parameters based on machine learning and human-in-the-loop approaches. One common theme in impedance adaptation is the degree of uncertainty. From the literature, we identify five key categories related to uncertainty and impedance control. 1) In learning from demonstration (LfD), uncertainty derived from the variability in demonstrations of trajectories can be used to infer lower robot stiffness, since the human demonstrator does not seem to care about precision in that section of motion [1]}, [2]}. 2) In LfD, the robot's available confidence in the skill is used, where stiffness can be reduced when entering unexplored regions as actions might be unsafe [3]}. 3) When faced with uncertainty in environment constraints in task execution, the robot should have lower stiffness in order not to produce dangerously high forces when the contact is unexpected [4]}, [5]}, [6]}. 4) In case of uncertain external perturbations the robot should increase the impedance to ensure the accuracy of position tracking task [7]}, [8]}. 5) When there is uncertainty in human goal during collaborative tasks, the robot should be compliant to let them determine a specific degree of freedom (DoF). For example, when the human should lead the collaborative carrying of an object [9]}, or when the human should guide a polishing machine that is carried by the robot [10]}.
